Future Farming in India A Playbook for Scaling Artificial Intelligence in Agriculture 2025

2.2 Lessons learned from AI use cases Working with AI programmes for agriculture has highlighted critical lessons that foreground the need for a framework to develop AI ecosystems.In light of these lessons, Section 3 describes a multistakeholder framework for scaling the use of AI for agriculture. Cultivators and farmers will gain most from AI if they have access to multiple use cases rather than specialized use cases only. For instance, a national rollout of pest management that increases productivity in the absence of smart markets can lead to market gluts. Similarly, use cases such as rapid soil analysis are key enablers for developing efficient macrocrop planning models. All AI-enabled agriculture use cases have some foundational enablers. This includes the need for data exchanges, data-sharing protocols, validation sandboxes, a strong last-mile delivery network, financing for adoption and a mechanism to collect data for continuous improvement of models. Investing in these foundational elements should take a portfolio view of use cases rather than viewing use cases in isolation. Deploying any AI use case at scale will require collaboration among different participants. For instance, research–industry collaboration is critical to ensure that models are well rooted in agricultural contexts. Similarly, agritech–financier collaboration is critical to ensure affordability and adoption of use cases.Use cases need to be integrated Cross-cutting foundational elements are critical Multistakeholder collaboration is essentialThree key lessons learned from AI use cases in agriculture FIGURE 11 Future Farming in India 27